From 8243cd72e4cd39b48a996615d8ba35d91e849f19 Mon Sep 17 00:00:00 2001 From: Sherin Date: Tue, 31 Dec 2024 20:50:08 +0200 Subject: [PATCH 1/2] Copied from DOC360 --- .../workloads/Run:ai_workload_types.md | 52 ++++++++++++++ .../workloads/Workloads_at_runai.md | 67 +++++++++++++++++++ 2 files changed, 119 insertions(+) create mode 100644 docs/Researcher/workloads/Run:ai_workload_types.md create mode 100644 docs/Researcher/workloads/Workloads_at_runai.md diff --git a/docs/Researcher/workloads/Run:ai_workload_types.md b/docs/Researcher/workloads/Run:ai_workload_types.md new file mode 100644 index 0000000000..1f7a1a4854 --- /dev/null +++ b/docs/Researcher/workloads/Run:ai_workload_types.md @@ -0,0 +1,52 @@ +# Run:ai workload types + +In the world of machine learning (ML), the journey from raw data to actionable insights is a complex process that spans multiple stages. Each stage of the AI lifecycle requires different tools, resources, and frameworks to ensure optimal performance. Run:ai simplifies this by offering specialized workload types tailored to each phase, facilitating a smooth transition across various stages of the ML workflows. + +The ML lifecycle usually begins with the experimental work on data and exploration of different modeling techniques to identify the best approach for accurate predictions. At this stage, resource consumption is usually moderate as experimentation is done on a smaller scale. As confidence grows in the model's potential and its accuracy, the demand for computing resources increases. This is especially true during the training phase, where vast amounts of data need to be processed, particularly with complex models like LLMs, with their huge parameter sizes, that often require distributed training across multiple GPUs to handle the intensive computational load. + +Finally, once the model is ready, it moves to the inference stage, where it is deployed to make predictions on new, unseen data. Run:ai's workload types are designed to correspond with the natural stages of this lifecycle. They are structured to align with the specific resource and framework requirements of each phase, ensuring that AI researchers and data scientists can focus on advancing their models without worrying about infrastructure management. + +Run:ai offers three workload types, that correspond to a specific phase of the researcher’s work: + +* Workspaces – Designed for experimentation with data and models. +* Training – For resource-intensive tasks such as model training and data preparation. +* Inference – For deploying and serving the trained model. + +## Workspaces: the experimentation phase + +The Workspace is where data scientists conduct initial research, experiment with different datasets, and test various algorithms. This is the most flexible stage in the ML lifecycle, where models and data are explored, tuned, and refined. The value of workspaces lies in the flexibility they offer allowing the researcher to iterate quickly without being constrained by rigid infrastructure. + +* Framework flexibility +Workspaces support a variety of machine learning frameworks, as researchers need to experiment with different tools and methods. + +* Resource requirements +Workspaces are often lighter on resources compared to the training phase, but they still require significant computational power for data processing, analysis, and model iteration. + +Hence, the default for the Run:ai workspaces considerations is to allow scheduling those workloads without the ability to preempt them once the resources were allocated. However, this non-preemptable state doesn’t allow to utilize more resources outside of the project’s deserved quota. + +Check the running workspaces to learn more about how to submit a workspace via the Run:ai UI. For quick starts check the Running Jupyter Notebook using workspaces. TBD + +## Training: scaling resources for model development + +As models mature and the need for more robust data processing and model training increases, Run:ai facilitates this shift through the Training workload. This phase is resource-intensive, often requiring distributed computing and high-performance clusters to process vast datasets and train models. + +* Training architecture +For the training workloads we allow to specify the type of architecture the workloads need to have - standard or distributed. The distributed architecture is relevant for larger datasets and more complex models that require utilizing multiple nodes. For the distributed architecture Run:ai allows the specification of different configuration for the master and the workers and supports the following frameworks - PyTorch, XG Boost, MPI, Tensor Flow. In addition, as part of the distributed configuration, Run:ai enable the researchers to schedule their distributed workloads on nodes within the same region, zone, placement group, or any other topology. + +* Resource requirements +Training tasks demand high memory, compute power, and storage. Run:ai ensures that the allocated resources match the scale of the task and allows those workloads to utilize more compute resources than the project’s deserved quota. Make sure that if you wish your training workload not to be preempted, specify the number of GPU’s that are in your quota. + +Check the Standard training and Distributed training to learn more about how to submit a training workload via the Run:ai UI. For quick starts check the Run your first standard training and Run your first distributed training. TBD + +## Inference: deploying and serving models + +Once a model is trained and validated, it moves to the Inference stage, where it is deployed to make predictions (usually in a production environment). This phase is all about efficiency and responsiveness, as the model needs to serve real-time or batch predictions to end-users or other systems. + +* Inference-specific use cases +Naturally, inference workloads are required to change and adapt to the ever-changing demands to meet SLA. For example, additional replicas may be deployed, manually or automatically, to increase compute resources as part of a horizontal scaling approach or a new version of the deployment may need to be rolled out without affecting the running services. + +* Resource requirements +Inference models differ in size and purpose, leading to varying computational requirements. For example, small OCR models can run efficiently on CPUs, whereas large language models (LLMs) typically require significant GPU memory for deployment and serving. Inference workloads are considered production-critical and are given the highest priority to ensure compliance with SLAs. Additionally, Run:ai ensures that inference workloads cannot be preempted, maintaining consistent performance and reliability. + +Check the Deploy a custom inference workload to learn more about how to submit an inference workload via the Run:ai UI. For quick starts check the Deploy Llama model + diff --git a/docs/Researcher/workloads/Workloads_at_runai.md b/docs/Researcher/workloads/Workloads_at_runai.md new file mode 100644 index 0000000000..5366546010 --- /dev/null +++ b/docs/Researcher/workloads/Workloads_at_runai.md @@ -0,0 +1,67 @@ +# Workloads at Run:ai + +Run:ai enhances visibility and simplifies management, by monitoring, presenting and orchestrating all AI workloads in the clusters it is installed on. Workloads are the fundamental building blocks for consuming resources, enabling AI practitioners such as researchers, data scientists and engineers to efficiently support the entire life cycle of an AI initiative. + +## Workloads across the AI lifecycle + +A typical AI initiative progresses through several key stages, each with distinct workloads and objectives. With Run:ai, research and engineering teams can host and manage all these workloads to achieve the following: + +* Data preparation: Aggregating, cleaning, normalizing, and labeling data to prepare for training. +* Training: Conducting resource-intensive model development and iterative performance optimization. +* Fine-tuning: Adapting pre-trained models to domain-specific datasets while balancing efficiency and performance. +* Inference: Deploying models for real-time or batch predictions with a focus on low latency and high throughput. +* Monitoring and optimization: Ensuring ongoing performance by addressing data drift, usage patterns, and retraining as needed. + +## What is a workload? + +A workload runs in the cluster, is associated with a namespace, and operates to fulfill its targets, whether that is running to completion for a batch job, allocating resources for experimentation in an integrated development environment (IDE) or a notebook, or serving inference requests in production. + +The workload, defined by the AI practitioner, consists of: + +* Container images: This includes the application, its dependencies, and the runtime environment. +* Compute resources: CPU, GPU, and RAM to execute efficiently and address the workload’s needs. +* Data sets: The data needed for processing, such as training data sets or input from external databases. +* Credentials: The access to certain data sources or external services, ensuring proper authentication and authorization. + +Workload scheduling and orchestration + +Run:ai’s core mission is to optimize AI resource usage at scale. This is achieved through efficient scheduling and orchestrating of all cluster workloads using the Run:ai Scheduler. The Scheduler allows the prioritization of workloads across different departments and projects within the organization at large scales, based on the resource distribution set by the system administrator. + +## Run:ai and third-party workloads + +Run:ai workloads and third-party workloads are supported: + +* Run:ai workloads: These workloads are submitted via the Run:ai platform. They are represented by Kubernetes Custom Resource Definitions (CRDs) and APIs. When using Run:ai workloads, a complete Workload and Scheduling Policy solution is offered for administrators to ensure optimizations, governance and security standards are applied. +* Third-party workloads: These workloads are submitted via third-party applications that use the Run:ai scheduler. The Run:ai platform manages and monitors these workloads. They enable seamless integrations with external tools, allowing teams and individuals flexibility. + +### Levels of support + +Different types of workloads have different levels of support. Understanding what capabilities are needed before selecting the workload type to work with is important. The table below details the level of support for each workload type in Run:ai. The Run:ai native workloads are fully supported with all of Run:ai advanced features and capabilities. While third-party workloads are partially supported. The list of capabilities can change between different Run:ai versions. + +| Functionality | Workload Type | | | | | +| ----- | :---: | :---: | :---: |:----------------------:| ----- | +| | Run:ai workloads | | | | Third-party workloads | +| | Training - Standard | Workspace | Inference | Training - distributed | All K8s workloads | +| [Fairness](../../../Researcher/scheduling/the-runai-scheduler.md#fairness-fair-resource-distribution) | v | v | v | v | v | +| [Priority and preemption](../../../Researcher/scheduling/the-runai-scheduler.md#preemption) | v | v | v | v | v | +| [Over quota](../../../Researcher/scheduling/the-runai-scheduler.md#over-quota-priority) | v | v | v | v | v | +| [Node pools](../../../platform-admin/aiinitiatives/resources/node-pools.md) | v | v | v | v | v | +| Bin packing / Spread | v | v | v | v | v | +| Fractions | v | v | v | v | v | +| Dynamic fractions | v | v | v | v | v | +| Node level scheduler | v | v | v | v | v | +| GPU swap | v | v | v | v | v | +| Elastic scaling | NA | NA | v | v | v | +| [Gang scheduling](../../../Researcher/scheduling/the-runai-scheduler.md#gang-scheduling) | v | v | v | v | v | +| [Monitoring](../../../admin/maintenance/alert-monitoring.md) | v | v | v | v | v | +| [RBAC](../../../admin/authentication/authentication-overview.md#role-based-access-control-rbac-in-runai) | v | v | v | v | | +| Workload awareness | v | v | v | v | | +| [Workload submission](../../../Researcher/workloads/overviews/managing-workloads.md) | v | v | v | v | | +| Workload actions (stop/run) | v | v | v | v | | +| [Policies](../../../platform-admin/workloads/policies/overview.md) | v | v | v | v | | +| [Scheduling rules](../../../platform-admin/aiinitiatives/org/scheduling-rules.md) | v | v | v | v | | + +!!! Note + __Workload awareness__ + + Specific workload-aware visibility, so that different pods are identified and treated as a single workload (for example GPU utilization, workload view, dashboards). From 3c7d60dd49ac9a8c6b6f9a097116f12ee2d0d82d Mon Sep 17 00:00:00 2001 From: Sherin Date: Fri, 3 Jan 2025 13:28:24 +0200 Subject: [PATCH 2/2] Copied workloads from Doc360 --- .../workloads/Run:ai_workload_types.md | 52 -------------- .../workloads/Workloads_at_runai.md | 67 ----------------- .../workloads/img/workload-table.png | Bin 0 -> 342532 bytes .../workloads}/managing-workloads.md | 4 +- docs/Researcher/workloads/overviews | 1 - docs/Researcher/workloads/workload-types.md | 58 +++++++++++++++ .../workloads/workloads-at-runai.md | 65 +++++++++++++++++ docs/platform-admin/aiinitiatives/overview.md | 3 + .../overviews/img/workload-table.png | Bin 78640 -> 0 bytes .../workloads/overviews/workload-support.md | 68 ------------------ mkdocs.yml | 5 +- 11 files changed, 131 insertions(+), 192 deletions(-) delete mode 100644 docs/Researcher/workloads/Run:ai_workload_types.md delete mode 100644 docs/Researcher/workloads/Workloads_at_runai.md create mode 100644 docs/Researcher/workloads/img/workload-table.png rename docs/{platform-admin/workloads/overviews => Researcher/workloads}/managing-workloads.md (98%) delete mode 120000 docs/Researcher/workloads/overviews create mode 100644 docs/Researcher/workloads/workload-types.md create mode 100644 docs/Researcher/workloads/workloads-at-runai.md delete mode 100644 docs/platform-admin/workloads/overviews/img/workload-table.png delete mode 100644 docs/platform-admin/workloads/overviews/workload-support.md diff --git a/docs/Researcher/workloads/Run:ai_workload_types.md b/docs/Researcher/workloads/Run:ai_workload_types.md deleted file mode 100644 index 1f7a1a4854..0000000000 --- a/docs/Researcher/workloads/Run:ai_workload_types.md +++ /dev/null @@ -1,52 +0,0 @@ -# Run:ai workload types - -In the world of machine learning (ML), the journey from raw data to actionable insights is a complex process that spans multiple stages. Each stage of the AI lifecycle requires different tools, resources, and frameworks to ensure optimal performance. Run:ai simplifies this by offering specialized workload types tailored to each phase, facilitating a smooth transition across various stages of the ML workflows. - -The ML lifecycle usually begins with the experimental work on data and exploration of different modeling techniques to identify the best approach for accurate predictions. At this stage, resource consumption is usually moderate as experimentation is done on a smaller scale. As confidence grows in the model's potential and its accuracy, the demand for computing resources increases. This is especially true during the training phase, where vast amounts of data need to be processed, particularly with complex models like LLMs, with their huge parameter sizes, that often require distributed training across multiple GPUs to handle the intensive computational load. - -Finally, once the model is ready, it moves to the inference stage, where it is deployed to make predictions on new, unseen data. Run:ai's workload types are designed to correspond with the natural stages of this lifecycle. They are structured to align with the specific resource and framework requirements of each phase, ensuring that AI researchers and data scientists can focus on advancing their models without worrying about infrastructure management. - -Run:ai offers three workload types, that correspond to a specific phase of the researcher’s work: - -* Workspaces – Designed for experimentation with data and models. -* Training – For resource-intensive tasks such as model training and data preparation. -* Inference – For deploying and serving the trained model. - -## Workspaces: the experimentation phase - -The Workspace is where data scientists conduct initial research, experiment with different datasets, and test various algorithms. This is the most flexible stage in the ML lifecycle, where models and data are explored, tuned, and refined. The value of workspaces lies in the flexibility they offer allowing the researcher to iterate quickly without being constrained by rigid infrastructure. - -* Framework flexibility -Workspaces support a variety of machine learning frameworks, as researchers need to experiment with different tools and methods. - -* Resource requirements -Workspaces are often lighter on resources compared to the training phase, but they still require significant computational power for data processing, analysis, and model iteration. - -Hence, the default for the Run:ai workspaces considerations is to allow scheduling those workloads without the ability to preempt them once the resources were allocated. However, this non-preemptable state doesn’t allow to utilize more resources outside of the project’s deserved quota. - -Check the running workspaces to learn more about how to submit a workspace via the Run:ai UI. For quick starts check the Running Jupyter Notebook using workspaces. TBD - -## Training: scaling resources for model development - -As models mature and the need for more robust data processing and model training increases, Run:ai facilitates this shift through the Training workload. This phase is resource-intensive, often requiring distributed computing and high-performance clusters to process vast datasets and train models. - -* Training architecture -For the training workloads we allow to specify the type of architecture the workloads need to have - standard or distributed. The distributed architecture is relevant for larger datasets and more complex models that require utilizing multiple nodes. For the distributed architecture Run:ai allows the specification of different configuration for the master and the workers and supports the following frameworks - PyTorch, XG Boost, MPI, Tensor Flow. In addition, as part of the distributed configuration, Run:ai enable the researchers to schedule their distributed workloads on nodes within the same region, zone, placement group, or any other topology. - -* Resource requirements -Training tasks demand high memory, compute power, and storage. Run:ai ensures that the allocated resources match the scale of the task and allows those workloads to utilize more compute resources than the project’s deserved quota. Make sure that if you wish your training workload not to be preempted, specify the number of GPU’s that are in your quota. - -Check the Standard training and Distributed training to learn more about how to submit a training workload via the Run:ai UI. For quick starts check the Run your first standard training and Run your first distributed training. TBD - -## Inference: deploying and serving models - -Once a model is trained and validated, it moves to the Inference stage, where it is deployed to make predictions (usually in a production environment). This phase is all about efficiency and responsiveness, as the model needs to serve real-time or batch predictions to end-users or other systems. - -* Inference-specific use cases -Naturally, inference workloads are required to change and adapt to the ever-changing demands to meet SLA. For example, additional replicas may be deployed, manually or automatically, to increase compute resources as part of a horizontal scaling approach or a new version of the deployment may need to be rolled out without affecting the running services. - -* Resource requirements -Inference models differ in size and purpose, leading to varying computational requirements. For example, small OCR models can run efficiently on CPUs, whereas large language models (LLMs) typically require significant GPU memory for deployment and serving. Inference workloads are considered production-critical and are given the highest priority to ensure compliance with SLAs. Additionally, Run:ai ensures that inference workloads cannot be preempted, maintaining consistent performance and reliability. - -Check the Deploy a custom inference workload to learn more about how to submit an inference workload via the Run:ai UI. For quick starts check the Deploy Llama model - diff --git a/docs/Researcher/workloads/Workloads_at_runai.md b/docs/Researcher/workloads/Workloads_at_runai.md deleted file mode 100644 index 5366546010..0000000000 --- a/docs/Researcher/workloads/Workloads_at_runai.md +++ /dev/null @@ -1,67 +0,0 @@ -# Workloads at Run:ai - -Run:ai enhances visibility and simplifies management, by monitoring, presenting and orchestrating all AI workloads in the clusters it is installed on. Workloads are the fundamental building blocks for consuming resources, enabling AI practitioners such as researchers, data scientists and engineers to efficiently support the entire life cycle of an AI initiative. - -## Workloads across the AI lifecycle - -A typical AI initiative progresses through several key stages, each with distinct workloads and objectives. With Run:ai, research and engineering teams can host and manage all these workloads to achieve the following: - -* Data preparation: Aggregating, cleaning, normalizing, and labeling data to prepare for training. -* Training: Conducting resource-intensive model development and iterative performance optimization. -* Fine-tuning: Adapting pre-trained models to domain-specific datasets while balancing efficiency and performance. -* Inference: Deploying models for real-time or batch predictions with a focus on low latency and high throughput. -* Monitoring and optimization: Ensuring ongoing performance by addressing data drift, usage patterns, and retraining as needed. - -## What is a workload? - -A workload runs in the cluster, is associated with a namespace, and operates to fulfill its targets, whether that is running to completion for a batch job, allocating resources for experimentation in an integrated development environment (IDE) or a notebook, or serving inference requests in production. - -The workload, defined by the AI practitioner, consists of: - -* Container images: This includes the application, its dependencies, and the runtime environment. -* Compute resources: CPU, GPU, and RAM to execute efficiently and address the workload’s needs. -* Data sets: The data needed for processing, such as training data sets or input from external databases. -* Credentials: The access to certain data sources or external services, ensuring proper authentication and authorization. - -Workload scheduling and orchestration - -Run:ai’s core mission is to optimize AI resource usage at scale. This is achieved through efficient scheduling and orchestrating of all cluster workloads using the Run:ai Scheduler. The Scheduler allows the prioritization of workloads across different departments and projects within the organization at large scales, based on the resource distribution set by the system administrator. - -## Run:ai and third-party workloads - -Run:ai workloads and third-party workloads are supported: - -* Run:ai workloads: These workloads are submitted via the Run:ai platform. They are represented by Kubernetes Custom Resource Definitions (CRDs) and APIs. When using Run:ai workloads, a complete Workload and Scheduling Policy solution is offered for administrators to ensure optimizations, governance and security standards are applied. -* Third-party workloads: These workloads are submitted via third-party applications that use the Run:ai scheduler. The Run:ai platform manages and monitors these workloads. They enable seamless integrations with external tools, allowing teams and individuals flexibility. - -### Levels of support - -Different types of workloads have different levels of support. Understanding what capabilities are needed before selecting the workload type to work with is important. The table below details the level of support for each workload type in Run:ai. The Run:ai native workloads are fully supported with all of Run:ai advanced features and capabilities. While third-party workloads are partially supported. The list of capabilities can change between different Run:ai versions. - -| Functionality | Workload Type | | | | | -| ----- | :---: | :---: | :---: |:----------------------:| ----- | -| | Run:ai workloads | | | | Third-party workloads | -| | Training - Standard | Workspace | Inference | Training - distributed | All K8s workloads | -| [Fairness](../../../Researcher/scheduling/the-runai-scheduler.md#fairness-fair-resource-distribution) | v | v | v | v | v | -| [Priority and preemption](../../../Researcher/scheduling/the-runai-scheduler.md#preemption) | v | v | v | v | v | -| [Over quota](../../../Researcher/scheduling/the-runai-scheduler.md#over-quota-priority) | v | v | v | v | v | -| [Node pools](../../../platform-admin/aiinitiatives/resources/node-pools.md) | v | v | v | v | v | -| Bin packing / Spread | v | v | v | v | v | -| Fractions | v | v | v | v | v | -| Dynamic fractions | v | v | v | v | v | -| Node level scheduler | v | v | v | v | v | -| GPU swap | v | v | v | v | v | -| Elastic scaling | NA | NA | v | v | v | -| [Gang scheduling](../../../Researcher/scheduling/the-runai-scheduler.md#gang-scheduling) | v | v | v | v | v | -| [Monitoring](../../../admin/maintenance/alert-monitoring.md) | v | v | v | v | v | -| [RBAC](../../../admin/authentication/authentication-overview.md#role-based-access-control-rbac-in-runai) | v | v | v | v | | -| Workload awareness | v | v | v | v | | -| [Workload submission](../../../Researcher/workloads/overviews/managing-workloads.md) | v | v | v | v | | -| Workload actions (stop/run) | v | v | v | v | | -| [Policies](../../../platform-admin/workloads/policies/overview.md) | v | v | v | v | | -| [Scheduling rules](../../../platform-admin/aiinitiatives/org/scheduling-rules.md) | v | v | v | v | | - -!!! 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basic unit of work in Run:ai. Researchers and Engineers use workloads for every stage in their AI [Project](../../../platform-admin/aiinitiatives/org/projects.md) lifecycle. Workloads can be used to build, train, or deploy a model. Run:ai supports all types of Kubernetes workloads. Researchers can work with any workload in their organization but will get the largest value working with Run:ai native workloads. - -Run:ai offers three native types of workloads: - -* [Workspace](../../../Researcher/workloads/workspaces/overview.md). Used for data preparation and model-building tasks. -* [Training](../../../Researcher/workloads/trainings.md). Used for training tasks. -* [Inference](../../../Researcher/workloads/inference-overview.md). Used for inference and model serving tasks - -Run:ai native workloads can be created via the Run:ai User interface, [API](https://api-docs.run.ai/2.18/tag/Workloads) or [Command-line interface](../../../Researcher/cli-reference/Introduction.md). - -## Levels of support - -Different types of workloads have different levels of support. Understanding what capabilities are needed before selecting the workload type to work with is important. The table below details the level of support for each workload type in Run:ai. The Run:ai native workloads are fully supported with all of Run:ai advanced features and capabilities. While third-party workloads are partially supported. The list of capabilities can change between different Run:ai versions. - -| Functionality | Workload Type | | | | | -| ----- | :---: | :---: | :---: |:----------------------:| ----- | -| | Run:ai workloads | | | | Third-party workloads | -| | Training - Standard | Workspace | Inference | Training - distributed | All K8s workloads | -| [Fairness](../../../Researcher/scheduling/the-runai-scheduler.md#fairness-fair-resource-distribution) | v | v | v | v | v | -| [Priority and preemption](../../../Researcher/scheduling/the-runai-scheduler.md#preemption) | v | v | v | v | v | -| [Over quota](../../../Researcher/scheduling/the-runai-scheduler.md#over-quota-priority) | v | v | v | v | v | -| [Node pools](../../../platform-admin/aiinitiatives/resources/node-pools.md) | v | v | v | v | v | -| Bin packing / Spread | v | v | v | v | v | -| Fractions | v | v | v | v | v | -| Dynamic fractions | v | v | v | v | v | -| Node level scheduler | v | v | v | v | v | -| GPU swap | v | v | v | v | v | -| Elastic scaling | NA | NA | v | v | v | -| [Gang scheduling](../../../Researcher/scheduling/the-runai-scheduler.md#gang-scheduling) | v | v | v | v | v | -| [Monitoring](../../../admin/maintenance/alert-monitoring.md) | v | v | v | v | v | -| [RBAC](../../../admin/authentication/authentication-overview.md#role-based-access-control-rbac-in-runai) | v | v | v | v | | -| Workload awareness | v | v | v | v | | -| [Workload submission](../../../Researcher/workloads/overviews/managing-workloads.md) | v | v | v | v | | -| Workload actions (stop/run) | v | v | v | v | | -| [Policies](../../../platform-admin/workloads/policies/overview.md) | v | v | v | v | | -| [Scheduling rules](../../../platform-admin/aiinitiatives/org/scheduling-rules.md) | v | v | v | v | | - -!!! Note - __Workload awareness__ - - Specific workload-aware visibility, so that different pods are identified and treated as a single workload (for example GPU utilization, workload view, dashboards). - - -## Workload scopes - -Workloads must be created under a [project](../../../platform-admin/aiinitiatives/org/projects.md). A project is the fundamental organization unit in the Run:ai account. To manage workloads, it’s required to first create a project or have one created by the administrator. - -## Policies and rules - -[Policies and rules](../../../platform-admin/workloads/policies/overview.md) empower administrators to establish default values and implement restrictions on workloads allowing enhanced control, assuring compatibility with organizational policies, and optimizing resource usage and utilization. - -## Workload statuses - -The following table describes the different phases in a workload life cycle. - -| Phase | Description | Entry condition | Exit condition | -| :---- | :---- | :---- | :---- | -| Creating | Workload setup is initiated in the Cluster. Resources and pods are now provisioning | A workload is submitted | A multi-pod group is created | -| Pending | Workload is queued and awaiting resource allocation. | A pod group exists | All pods are scheduled | -| Initializing | Workload is retrieving images, starting containers, and preparing pods | All pods are scheduled | All pods are initialized or a failure to initialize is detected | -| Running | Workload is currently in progress with all pods operational | All pods initialized (all containers in pods are ready) | workload completion or failure | -| Degraded | Pods may not align with specifications, network services might be incomplete, or persistent volumes may be detached. Check your logs for specific details. | Pending: All pods are running but with issues Running: All pods are running with no issues. | Running: All resources are OK Completed: Workload finished with fewer resources Failed: Workload failure or user-defined rules | -| Deleting | Workload and its associated resources are being decommissioned from the cluster | Deleting the workload. | Resources are fully deleted | -| Stopped | The workload is on hold and resources are intact but inactive | Stopping the workload without deleting resources | Transitioning back to the initializing phase or proceeding to deleting the workload | -| Failed | Image retrieval failed or containers experienced a crash. Check your logs for specific details. | An error occurs preventing the successful completion of the workload | Terminal State | -| Completed | Workload has successfully finished its execution | The workload has finished processing without errors | Terminal State | - diff --git a/mkdocs.yml b/mkdocs.yml index c702ea02cb..be9c51f38e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -352,8 +352,9 @@ nav: - 'Over-Quota, Basic Fairness & Bin-Packing' : 'Researcher/Walkthroughs/walkthrough-overquota.md' - 'Queue Fairness' : 'Researcher/Walkthroughs/walkthrough-queue-fairness.md' - 'Workloads' : - - 'Managing Workloads' : 'Researcher/workloads/overviews/managing-workloads.md' - - 'Workload Support' : 'Researcher/workloads/overviews/workload-support.md' + - 'Workload at Run:ai' : 'Researcher/workloads/workloads-at-runai.md' + - "Run:ai workload types": 'Researcher/workloads/workload-types.md' + - 'Managing Workloads' : 'Researcher/workloads/managing-workloads.md' - 'Workload Assets' : - 'Overview' : 'Researcher/workloads/assets/overview.md' - 'Environments' : 'Researcher/workloads/assets/environments.md'